Decentralized Federated Learning for IoT Malware Detection at the Multi-Access Edge: A Two-Tier, Privacy-Preserving Design

Botnet attacks on Internet of Things (IoT) devices are escalating at the 5G/6G multi-access edge, yet most federated learning frameworks for IoT malware detection (FL-IMD) still hinge on a central aggregator, enlarging the attack surface, weakening privacy, and creating a single point of failure. We...

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Bibliografiske detaljer

Principais autores:	Mohammed Asiri, Maher A. Khemakhem, Reemah M. Alhebshi, Bassma S. Alsulami, Fathy E. Eassa
Format:	Artigo
Sprog:	Inglês
Udgivet:	MDPI AG 2025-10-01
Serier:	Future Internet
Fag:	federated learning decentralized learning multi-access edge computing (MEC) Internet of Things (IoT) IoT malware detection privacy preservation
Online adgang:	https://www.mdpi.com/1999-5903/17/10/475
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